Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

SECTION 11.1 | Introduction to Repeated-Measures Designs 337
The main advantage of a repeated-measures study is that it uses exactly the same
individuals in all treatment conditions. Thus, there is no risk that the participants in one
treatment are substantially different from the participants in another. With an independentmeasures
design, on the other hand, there is always a risk that the results are biased because
the individuals in one sample are systematically different (smarter, faster, more extroverted,
and so on) than the individuals in the other sample. At the end of this chapter, we present a
more detailed comparison of repeated-measures studies and independent-measures studies,
considering the advantages and disadvantages of both types of research.
■ The Matched-Subjects Design
Occasionally, researchers try to approximate the advantages of a repeated-measures design
by using a technique known as matched subjects. A matched-subjects design involves two
separate samples, but each individual in one sample is matched one-to-one with an individual
in the other sample. Typically, the individuals are matched on one or more variables
that are considered to be especially important for the study. For example, a researcher
studying verbal learning might want to be certain that the two samples are matched in
terms of IQ. In this case, a participant with an IQ of 120 in one sample would be matched
with another participant with an IQ of 120 in the other sample. Although the participants in
one sample are not identical to the participants in the other sample, the matched-subjects
design at least ensures that the two samples are equivalent (or matched) with respect to a
specific variable.
DEFINITION
In a matched-subjects study, each individual in one sample is matched with an
individual in the other sample. The matching is done so that the two individuals
are equivalent (or nearly equivalent) with respect to a specific variable that the
researcher would like to control.
Of course, it is possible to match participants on more than one variable. For example,
a researcher could match pairs of subjects on age, gender, race, and IQ. In this case, for
example, a 22-year-old white female with an IQ of 115 who was in one sample would be
matched with another 22-year-old white female with an IQ of 115 in the second sample.
The more variables that are used, however, the more difficult it is to find matching pairs.
The goal of the matching process is to simulate a repeated-measures design as closely as
possible. In a repeated-measures design, the matching is perfect because the same individual
is used in both conditions. In a matched-subjects design, however, the best you can
get is a degree of match that is limited to the variable(s) that are used for the matching
process.
In a repeated-measures design or a matched-subjects design comparing two treatment
conditions, the data consist of two sets of scores, which are grouped into sets of
two, corresponding to the two scores obtained for each individual or each matched pair
of subjects (Table 11.1). Because the scores in one set are directly related, one-to-one,
with the scores in the second set, the two research designs are statistically equivalent
and share the common name related-samples designs (or correlated-samples designs).
In this chapter, we focus our discussion on repeated-measures designs because they are
overwhelmingly the more common example of related-samples designs. However, you
should realize that the statistical techniques used for repeated-measures studies also can
be applied directly to data from a matched-subjects study. We should also note that a
matched-subjects study occasionally is called a matched samples design, but the subjects